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We present a new algorithm for solving the block matching problem which is independent of image content and is faster than other full-search methods. The method employs a novel data structure called the windowed-sum-squared-table, and uses the fast Fourier transform (FFT) in its computation of the sum squared difference (SSD) metric. Use of the SSD metric allows for higher peak signal to noise ratios than other fast block matching algorithms which require the sum of absolute difference (SAD) metric. However, because of the complex floating point and integer math used in our computation of the SSD metric, our method is aimed at software implementations only. Test results show that our method has a running time 13%-29% of that for the exhaustive search, depending on the size of the search range.
Kilthau et al. (2003) studied this question.